Ramon,
Have you tried another driver to determine if the problem is in the Python
driver?
You can deserialize your composite key using the following code:
ByteBuffer t =
ByteBufferUtil.hexToBytes("0008000e70451f6404000500");
short periodlen = t.getShort
Hi,
we are running an application which produces every night a batch with
several hundreds of Gigabytes of data. Once a batch has been computed,
it is never modified (nor updates nor deletes), we just keep producing
new batches every day.
Now, we are *sometimes* interested to remove a comple
Peer,
that talks about having a similar sized cluster, I was wondering if there
is a way for moving from larger to smaller cluster. I will try a few things
as soon as i get time and update here.
On Thu, Nov 19, 2015 at 5:48 PM, Peer, Oded wrote:
> Have you read the DataStax documentation?
>
>
>
Hello Carlos and Oded,
Thanks to you all for your input!
@Carlos, I did not try the thrift client yet.
@Oded, thank you for deserializing the key. It looks exactly what to
expect, once it's deserialized...
I think we're onto something. I reproduced and simplified the case like
this. First I create
How often is sometimes - closer to 20% of the batches or 2%?
How are you querying batches, both current and older ones?
As always, your queries should drive your data models.
If deleting a batch is very infrequent, maybe best to not do it and simply
have logic in the app to ignore deleted batche
Hi all,
If I have the following table:
CREATE TABLE t (
pk int,
ck int,
c1 int,
c2 int,
...
PRIMARY KEY (pk, ck)
)
There are lots of non-clustering columns (1000+). From time to time I need
to do a query like this:
SELECT c1 FROM t WHERE pk = abc AND ck > xyz;
How efficient is this
As always, your queries should drive your data model. Unless you really
need 1000+ columns for most queries, you should consider separate tables
for the subsets of the columns that need to be returned for a given query.
The new 3.0 Materialized View feature can be used to easily create subsets
of
On behalf of the development community, I am pleased to announce the
release of YCSB 0.5.0.
Highlights:
Added support for Kudu (getkudu.io).
Added CQL support for Cassandra 2.1+, via the cassandra2-cql binding.
Improved semantics for scans under JDBC.
Replaced numeric return codes with meaningful
I can think of following features to solve
1. If you know the time period of after how long data should be removed then
use TTL feature2. Use Time Series to model the data and use inverted index to
query the data by time period? Naidu Saladi
On Tuesday, November 24, 2015 6:49 AM, Jack Kr
And DateTieredCompactionStrategy can be used to efficiently remove whole
sstables when the TTL expires, but this implies knowing what TTL to set in
advance.
I don't know if there are any tools to bulk delete older than a specific
age when DateTieredCompactionStrategy is used, but it might be a nic
Are all or ost of the 1000+ columns populated for a given row? If they are
sparse you can replace them with a single map collection column which would
only occupy the entries that are populated.
-- Jack Krupansky
On Tue, Nov 24, 2015 at 11:04 AM, Jack Krupansky
wrote:
> As always, your queries
If it's sparsely populated you'll get the same benefit from the schema
definition. You don't pay for fields you don't use.
> On Nov 24, 2015, at 12:17 PM, Jack Krupansky wrote:
>
> Are all or ost of the 1000+ columns populated for a given row? If they are
> sparse you can replace them with a
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